Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Useful Tools and Tips

In this section, you will first learn the importance of different splits of the dataset. After that, you learn some tips that will come handy when you start working on datasets that haven't been processed before. Then come tools such as pandas profiling and TensorBoard, which will make your life easier by providing easy access to information. We will take a look at AutoML and how it can be used to get high-performance models without much manual effort. Finally, we will visualize our Keras model and export the model diagram to a file.

Train, Development, and Test Datasets

We briefly talked about train, development, and test datasets in the previous chapters. Here, we will delve deeper into the topic.

The training, or train set is a sample from the dataset, and we use this to create our machine learning models. The development, or dev set (also known as validation set), is a sample that helps us tune the hyperparameters of the created model. The testing or test set...